THREATS AND CRISIS EVENTS DETECTION USING MACHINE LEARNING TECHNIQUES WITH SOCIAL MEDIA DATA

Abstract

In the paper, the authors present the outcome of web scraping software allowing for the automated classification of threats and crisis events detection. In order to improve the safety and comfort of human life, an analysis was made to quickly detect threats using a modern information channel such as social media. For this purpose, social media services that are popular in the examined region were reviewed and the appropriate ones were selected using the criteria of accessibility and popularity. Approximately 300 unique posts from local groups of cities and other administrative centers were collected and analyzed. The decision of which entry was classified as a threat was defined using the ChatGPT tool and the human expert. Both variants were tested using machine learning (ML) methods. The paper tested whether the ChatGPT tool would be effective at detecting presumed events and compared this approach to the classic ML approach.